Diagnostic imaging methods frequently rely on evidence of anatomical disturbances that confer specific information as to the presence or absence of disease. In the case of functional techniques, such as obtained using magnetic resonance, optical or bioelectric methods, evidence of disease is based on identifying some form of functional disturbance, either under conditions of a resting state or from an evoked response. In the field of neuroimaging, both sets of measures have contributed significantly to our understanding of brain function and of the impact of disease or trauma.
Favoring the use of optical methods for the detection of breast cancer, particularly in the near infrared region, are disturbances originating from tumor angiogenesis, which result in a malformed vascular bed, leading to disturbances in the hemoglobin signal. Knowledge of this has formed the basis of a large number of instrument development and clinical investigations aimed at extracting biomarkers sensitive to these disturbances. Publications that demonstrate such instrument include: B. J. Tromberg, N. Shah, R. Lanning, A. Cerussi, J. Espinoza, T. Pham, L. Svaasand, and J. Butler, “Non-invasive in vivo characterization of breast tumors using photon migration spectroscopy,” Neoplasia 2, 26-40 (2000); H. Jiang, Y. Xu, N. Iftimia, J. Eggert, K. Klove, L. Baron, and L. Fajardo, “Three-dimensional optical tomographic imaging of breast in a human subject,” IEEE Transactions on Medical Imaging 20, 1334-1340 (2001); R. Choe, S. D. Konecky, A. Corlu, K. Lee, T. Durduran, D. R. Busch, S. Pathak, B. J. Czerniecki, J. Tchou, D. L. Fraker, A. Demichele, B. Chance, S. R. Arridge, M. Schweiger, J. P. Culver, M. D. Schnall, M. E. Putt, M. A. Rosen, and A. G. Yodh, “Differentiation of benign and malignant breast tumors by in-vivo three-dimensional parallel-plate diffuse optical tomography,” J. Biomedical Optics 14, 024020 (2009); Q. Fang, S. A. Carp, J. Selb, G. Boverman, Q. Zhang, D. B. Kopans, R. H. Moore, E. L. Miller, D. H. Brooks, and D. A. Boas, “Combined optical imaging and mammography of the healthy breast: optical contrast derived from breast structure and compression,” IEEE Transactions on Medical Imaging 28, 30-42 (2009); and S. M. van de Ven, S. G. Elias, A. J. Wiethoff, M. van der Voort, T. Nielsen, B. Brendel, C. Bontus, F. Uhlemann, R. Nachabe, R. Harbers, M. van Beek, L. Bakker, M. B. van der Mark, P. Luijten, and W. P. Mali, “Diffuse optical tomography of the breast: preliminary findings of a new prototype and comparison with magnetic resonance imaging,” European Radiology 19, 1108-1113 (2009). Commonly considered are measures that determine the content of hemoglobin as well as other constituents (e.g., tissue water or lipid content). Discussion on this subject can be found in, for example, S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “Interpreting hemoglobin and water concentration, oxygen saturation, and scattering measured in vivo by near-infrared breast tomography,” Proc. National Academy of Sciences USA 100, 12349-12354 (2003) and S. Srinivasan, B. W. Pogue, S. Jiang, H. Dehghani, C. Kogel, S. Soho, J. J. Gibson, T. D. Tosteson, S. P. Poplack, and K. D. Paulsen, “In vivo hemoglobin and water concentrations, oxygen saturation, and scattering estimates from near-infrared breast tomography using spectral reconstruction,” Academic Radiology 13, 195-202 (2006). These measures are typically performed without regard to any temporal behavior associated with the hemoglobin signal that may be observable (e.g., presence of natural vascular rhythms).
An alternative approach is to perform optical studies for the purpose of exploring the temporal dynamics of the hemoglobin signal. Prior publications that describe this approach include, for example, P. Schneider, S. Piper, C. H. Schmitz, N. F. Schreiter, N. Volkwein, L. Lüdemann, U. Malzahn, A. Poellinger, “Fast 3D near-infrared breast imaging using indocyanine green for detection and characterization of breast lesions,” Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 183, 956-963 (2011); N. F. Schreiter, N. Volkwein, P. Schneider, M. H. Maurer, S. Piper, C. H. Schmitz, and A. Poellinger, “Optical imaging of breast cancer using hemodynamic changes induced by Valsalva maneuver,” Fortschritte auf dem Gebiet der Rontgenstrahlen und der bildgebenden Verfahren 184, 358-366 (2013). Indeed, consideration of such measures forms the basis of the method of pulse oximetry. While both static and dynamic optical measures of the breast have been implemented, there is a host of factors associated with how measures are taken, the natural variability in breast size and composition, and how data are explored, that have confounded efforts to identify useful biomarkers for the presence of breast cancer without the need for prior information. Most desirable would be to implement a simplified sensing and analysis strategy that is mainly robust to the details of these factors, but nevertheless can reliably yield biometrics that serve to detect and locate the presence of cancerous tumors of the breast.
Similar to the experience gained with bioelectric phenomena of tissue, measures of hemodynamics using optical methods can be explored with the goal of identifying phenomena that either are or are not directly observable. Non-observable phenomena often are the domain of inverse solvers used to derive spatial maps of background tissue coefficients. In the case of bioelectric studies, such efforts are often directed at identifying loci of aberrant neural activity such as those associated with epileptic lesions. Corresponding efforts applied to optical studies of tissue typically seek to generate spatial maps of the background optical absorption and scattering coefficients, from which can be further derived maps of components of the hemoglobin signal, other naturally occurring constituents (i.e., water, lipid content) and features associated with light scattering phenomena. R. Choe et al. (See above); Q. Fang et al. (See above), works of S. Srinivasan et al. (See above), and J. Wang, B. W. Pogue, S. Jiang, and K. D. Paulsen, “Near-infrared tomography of breast cancer hemoglobin, water, lipid, and scattering using combined frequency domain and cw measurement,” Optics Letters 35, 82-84 (2010) describe this approach.
Experience in other fields has shown that problems of this type, generally referred to as boundary value problems, can be notoriously difficult solve in any stable way. Should the goal be to derive biometrics based on absolute values of tissue constituents, then much detail regarding the particulars of the boundary conditions is required. For an appendage such as the breast, the natural variance in size, internal composition, and deformability make efforts to reliably define the boundary conditions needed to yield stable inverse solutions especially difficult.
In part because of these difficulties, efforts to apply such methods to extract biomarkers have been limited to measures of the hemoglobin signal that are time-independent. Even among these, the natural variance in breast size and composition has made rigorous efforts to apply use of recursive solvers mainly infeasible. As a consequence, efforts to employ inverse solvers frequently adopt use of simplifying methods, some of which are described in S. B. Colak, M. B. van der Mark, G. W. Hooft, J. H. Hoogenraad, E. S. van der Linden, and F. A. Kuijpers, “Clinical optical tomography and NIR spectroscopy for breast cancer detection,” IEEE J. Quantum Electronics 51, 1143-1158 (1999); J. P. Culver, R. Choe, M. J. Holboke, L. Zubkov, T. Durduran, A. Slemp, V. Ntziachristos, B. Chance, and A. G. Yodh, “Three-dimensional diffuse optical tomography in the parallel plane transmission geometry: evaluation of a hybrid frequency domain/continuous wave clinical system for breast imaging,” Medical Physics 30, 235-247 (2003); and H. Dehghani, M. M. Doyley, B. W. Pogue, S. Jiang, J. Geng, and K. D. Paulsen, “Breast deformation modelling for image reconstruction in near infrared optical tomography,” Physics in Medicine and Biology 49, 1131-1145 (2004). A common approach has been to use prior knowledge of reference, unaffected tissue to a region under analysis for which, frequently, the latter is strongly suspected to have cancer. Methods of using prior knowledge for this purpose is described, for example, in N. F. Schreiter et al. (See above); V. Ntziachristos, A. G. Yodh, M. D. Schnall, and B. Chance, “MRI-guided diffuse optical spectroscopy of malignant and benign breast lesions,” Neoplasia 4, 347-354 (2002); A. Li, E. L. Miller, M. E. Kilmer, T. J. Brukilacchio, T. Chaves, J. Stott, Q. Zhang, T. Wu, M. Chorlton, R. H. Moore, D. B. Kopans, and D. A. Boas, “Tomographic optical breast imaging guided by three-dimensional mammography,” Applied Optics 42, 5181-5190 (2003); Q. Zhu, “Optical tomography with ultrasound localization: initial clinical results and technical challenges,” Technology in Cancer Research & Treatment 4, 235-244 (2005); R. X. Xu, D. C. Young, J. J. Mao, and S. P. Povoski, “A prospective pilot clinical trial evaluating the utility of a dynamic near-infrared imaging device for characterizing suspicious breast lesions,” Breast Cancer Research 9, R88 (2007); and C. M. Carpenter, R. Rakow-Penner, S. Jiang, B. L. Daniel, B. W. Pogue, G. H. Glover, and K. D. Paulsen, “Inspired gas-induced vascular change in tumors with magnetic-resonance-guided near-infrared imaging: human breast pilot study,” J Biomedical Optics 15, 036026 (2010). While use of such methods does enhance the capability of distinguishing tumors from unaffected tissue, it also reduces their potential practical utility, especially if the goal is to derive biometrics that can serve as a primary screening tool.
An alternative approach to static measures is to explore the naturally occurring dynamics associated with the hemoglobin signal. Experience with other forms of dynamic measures (e.g., bioelectric measures of the brain or heart) indicates that there are a host of factors that can influence signal dynamics, thereby confounding interpretation of potential biomarkers. In the case of optical measures of the hemoglobin signal, time-varying changes can be expected from spontaneous or evoked changes in cardiovascular tone due to effects of posture, presence of commonly prevalent morbidities (e.g., atherosclerosis) and other factors, each of which can be further modified by local variations due to vascular autoregulation. Additionally, because these factors are uncorrelated across individuals, it can be expected that the natural variance associated with dynamics of the hemoglobin signal will be large in any group of individuals. Taken together, it can be expected that natural variance associated with optical measures of the breast should be very large indeed. Such excessive variance can be expected to complicate efforts to derive useful biometrics based on dynamic measures of the breast.
Experience with other forms of functional measures of tissue dynamics (e.g., EEG measures) has emphasized the value of implementing some form of a reference measure that serves to limit the impact of various naturally occurring confounding factors. One consideration regarding a reference measure would be to perform a simultaneous bilateral measurement, wherein information obtained from one breast is used as a reference for the other. Simplest would be to perform a time-independent measurement, in which case the influence of the natural vascular rhythms should be minimal. Certainly, simultaneous bilateral breast measures are routinely performed using the MR method. Here too, however, a brief consideration of the expected impact of natural variances in breast size and composition—for instance, it is known that the left breast is typically larger than the right breast; see D. Scutt, G. A. Lancaster, and J. T. Manning, “Breast asymmetry and predisposition to breast cancer,” Breast Cancer Research 8, R14 (2006).—on optical measures suggest that inter-subject variances could be very large.
Yet other factors affecting optical measures are the fidelity of optode contact. Being highly deformable, the details of contact could be expected to vary considerably across individuals, depending on measurement geometry. Moreover, the details of contact can be expected to vary even in cases of simple geometries such as a planar arrangement, because even small differences in contact pressure can affect the hemoglobin signal. See, for example, S. D. Jiang, B. W. Pogue, and K. D. Paulsen, “In vivo near infrared spectral detection of pressure-induced changes in breast tissue,” Optics Letters 28, 1212-1214 (2003), and A. L. Darling, P. K. Yalavarthy, M. M. Doyley, H. Dehghani, and B. W. Pogue, “Interstitial fluid pressure in soft tissue as a result of an externally applied contact pressure,” Physics in Medicine and Biology 52, 4121-4136 (2007). An expected confounding factor here is the natural variance in breast stiffness, which would cause regions near the chest wall to experience greater loads than regions closer to the nipple. Also confounding is the simple recognition that the signal attenuation with the optical technique is approximately a factor of ten per centimeter. Thus even small variations is breast size, contour, internal composition, or fidelity of optode contact can be expected to significantly influence the measured signal.
These considerations demonstrate that there are many naturally occurring factors, and elements of how data are obtained and treated, that can be expected to complicate the fidelity of derived biomarkers for the presence of cancer based on optical measures.